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IJMLC 2020 Vol.10(6): 746-751 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.6.1000

Fuzzy AHP and TOPSIS in Cross Domain Collaboration Recommendation with Fuzzy Visualization Representation

Maslina Zolkepli and Teh Noranis Mohd. Aris

Abstract—Cross domain collaboration recommendation method is proposed by combining fuzzy Analytic Hierarchy Process (AHP), fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and fuzzy network graph for interactive visualization method. Existing cross-domain recommendation tackles the problem of sparsity, scalability, cold-start and serendipity issues found in single-domain, therefore the combination of fuzzy AHP and TOPSIS with visualization method may be able to give decision makers a quick start to initiate cross-domain collaborations. The proposed method is applied to the DBLP bibliographic citation dataset that consists of 10 domains in the field of computer science. Results show that the combination of fuzzy AHP and TOPSIS enables decision makers to find several authors from across domains that consist of 2.2 million publications in less than 3 minutes. The combination method will be represented in fuzzy visualization technique for fuzzy data. The establishment of the cross domain recommendation will set a stage for efficient preparation for researchers who are interested to venture into other domains to increase their research competency.

Index Terms—Cross domain recommendation, fuzzy AHP, fuzzy TOPSIS, fuzzy visualization, recommendation system.

The authors are with the Dept. of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia (e-mail: masz@upm.edu.my, nuranis@upm.edu.my).


Cite: Maslina Zolkepli and Teh Noranis Mohd. Aris, "Fuzzy AHP and TOPSIS in Cross Domain Collaboration Recommendation with Fuzzy Visualization Representation," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 746-751, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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